We researched, analyzed and predicted building energy consumption data using cloud computing and constructed an intelligent model. A local outlier factor outlier discovery algorithm was created to monitor abnormal energy consumption. A random forest algorithm was used for high-dimensional data to predict building energy consumption and analyze data in the Commercial Building Energy Consumption Survey database. The degree of importance of independent variables was evaluated to analyze how the architectural attributes of office buildings affect energy consumption.
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